This pipeline has many dials to turn. We ran experiments with the res4 and res5 layers, different post-processing methods including, Regional Maximum Activations of Convolutions (R-MAC) [1]}, [2]}, Generalized-Mean (GeM) pooling [3]}, [4]}, and Average and Max Pooling, which are special cases of GeM pooling. We also swept over different image sizes, PCA dimensions, and R-MAC spatial levels. Our best results typically used the res4 layer with R-MAC spatial level 3, while the best PCA dimension and image size depended on the model. When using R-MAC, we trained the PCA on the full max pool matrix of the different crops. When applying PCA on the database and query datasets, we apply PCA on the same full max pool matrix of the different crops before summing the crops and normalizing the output as in the R-MAC algorithm. All of the code used is available in the open source VISSL library [5]}.
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